A curated list of multi-object-tracking and related area resources. It only contains online methods. 中文版更为详细，具体查看仓库根目录下的README-zh.md文件。
Multiple Object Tracking: A Literature Review [paper]
Deep Learning in Video Multi-Object Tracking: A Survey [paper]
Tracking the Trackers: An Analysis of the State of the Art in Multiple Object Tracking [paper]
Machine Learning Methods for Data Association in Multi-Object Tracking [paper]]
MOTChallenge: A Benchmark for Single-camera Multiple Target Tracking [paper] new paper for new MOT researcher
TPAGT: FGAGT: Tracklets Predicting Based Adaptive Graph Tracking [paper] original FGAGT
GSDT: Joint Object Detection and Multi-Object Tracking with Graph Neural Networks [paper]
SMOT: SMOT: Single-Shot Multi Object Tracking [paper]
CSTrack: Rethinking the competition between detection and ReID in Multi-Object Tracking [paper]
MAT: MAT: Motion-Aware Multi-Object Tracking [paper]
UnsupTrack: Simple Unsupervised Multi-Object Tracking [paper]
FairMOT: FairMOT: On the Fairness of Detection and Re-Identification in Multiple Object Tracking [code][paper] a new version of FairMOT, compared with new method like CTracker
DMM-Net: Simultaneous Detection and Tracking with Motion Modelling for Multiple Object Tracking [code][paper]
SoDA: SoDA: Multi-Object Tracking with Soft Data Association [[code]][paper]
CTracker: Chained-Tracker: Chaining Paired Attentive Regression Results for End-to-End Joint Multiple-Object Detection and Tracking [code][paper]
MPNTracker: Learning a Neural Solver for Multiple Object Tracking [code][paper]
UMA: A Unified Object Motion and Affinity Model for Online Multi-Object Tracking [code][paper]
RetinaTrack: Online Single Stage Joint Detection and Tracking [[code]][paper]
FairMOT: A Simple Baseline for Multi-Object Tracking [code][paper]
TubeTK: TubeTK: Adopting Tubes to Track Multi-Object in a One-Step Training Model [code][paper]
CenterTrack: Tracking Objects as Points [code][paper]
Lif_T: Lifted Disjoint Paths with Application in Multiple Object Tracking [code][paper]
PointTrack: Segment as points for efficient online multi-object tracking and segmentation [code][paper]
PointTrack++: PointTrack++ for Effective Online Multi-Object Tracking and Segmentation [code][paper]
FFT: Multiple Object Tracking by Flowing and Fusing [paper]
MIFT: Refinements in Motion and Appearance for Online Multi-Object Tracking [code][paper]
EDA_GNN: Graph Neural Based End-to-end Data Association Framework for Online Multiple-Object Tracking [code][paper]
GNMOT: Graph Networks for Multiple Object Tracking [code][paper]
Tracktor/Tracktor++: Tracking without bells and whistles [code][paper]
DeepMOT: How To Train Your Deep Multi-Object Tracker [code][paper]
JDE: Towards Real-Time Multi-Object Tracking [code][paper]
MOTS: MOTS: Multi-Object Tracking and Segmentation[paper]
FANTrack: FANTrack: 3D Multi-Object Tracking with Feature Association Network [code][paper]
FAMNet: Joint Learning of Feature, Affinity and Multi-dimensional Assignment for Online Multiple Object Tracking[paper]
DeepCC: Features for Multi-Target Multi-Camera Tracking and Re-Identification [paper]
SADF: Online Multi-Object Tracking with Historical Appearance Matching and Scene Adaptive Detection Filtering [paper]
DAN: Deep Affinity Network for Multiple Object Tracking [code][paper]
DMAN: Online Multi-Object Tracking with Dual Matching Attention Networks [code][paper]
MOTBeyondPixels: Beyond Pixels: Leveraging Geometry and Shape Cues for Online Multi-Object Tracking [code][paper]
MOTDT: Real-time Multiple People Tracking with Deeply Learned Candidate Selection and Person Re-Identification [code][paper]
DetTA: Detection-Tracking for Efficient Person Analysis: The DetTA Pipeline [code][paper]
V-IOU: Extending IOU Based Multi-Object Tracking by Visual Information [code][paper]
DeepSORT: Simple Online and Realtime Tracking with a Deep Association Metric [code][paper]
NMGC-MOT: Non-Markovian Globally Consistent Multi-Object Tracking [code][paper]
IOUTracker: High-Speed tracking-by-detection without using image information [code][paper]
RNN_LSTM: Online Multi-Target Tracking Using Recurrent Neural Networks [code][paper]
D2T: Detect to Track and Track to Detect [code][paper]
RCMSS: Online multi-object tracking via robust collaborative model and sample selection [paper]
towards-reid-tracking: Towards a Principled Integration of Multi-Camera Re-Identification and Tracking through Optimal Bayes Filters [code][paper]
CIWT: Combined image-and world-space tracking in traffic scenes [code][paper]
SORT: Simple online and realtime tracking [code][paper]
POI: POI: Multiple Object Tracking with High Performance Detection and Appearance Feature [code]
PETS 2009 Benchmark Data [url]
MOT Challenge [url]
UA-DETRAC [url]
WILDTRACK [url]
NVIDIA AI CITY Challenge [url]
VisDrone [url]
JTA Dataset [url]
Path Track [url]
TAO [url]
KITTI-Tracking [url]
APOLLOSCAPE [url]
APOLLO MOTS [url]
Omni-MOT [url]
| metric | formula |
|---|---|
| accuracy | $ Accuracy = {{TP + TN} \over {TP + TN + FP + FN}} $ |
| recall | $ Recall = {TP \over {TP + FN}} = TPR$ |
| precision | $ Precision = {TP \over {TP + FP}} $ |
| MA | $ MA = {FN \over {TP + FN}} $ |
| FA | $ FA = {FP \over {TP + FP}} $ |
| MOTA | |
| MOTP | $ MOTP = {\sum_{t,i}d_t^i \over \sum_tc_t }$ |
| IDP | $ IDP = {IDTP \over {IDTP + IDFP}} $ |
| IDR | $ IDR = {IDTP \over {IDTP + IDFN}} $ |
| IDF1 | $ IDF1 = {2 \over {{1 \over IDP} + {1 \over IDR}}} = {2IDTP \over {2IDTP + IDFP + IDFN}} $ |
| Rank | Model | MOTA | Paper | Year |
|---|---|---|---|---|
| 1 | FairMOT | 68.7 | A Simple Baseline for Multi-Object Tracking | 2020 |
| 2 | JDE | 64.4 | Towards Real-Time Multi-Object Tracking | 2019 |
| 3 | Lif_T | 61.3 | Lifted Disjoint Paths with Application in Multiple Object Tracking | 2020 |
| 4 | MPNTrack | 58.6 | Learning a Neural Solver for Multiple Object Tracking | 2020 |
| 5 | DeepMOT-Tracktor | 54.8 | How To Train Your Deep Multi-Object Tracker | 2019 |
| 6 | TNT | 49.2 | Exploit the Connectivity: Multi-Object Tracking with TrackletNet | 2018 |
| 7 | GCRA | 48.2 | Trajectory Factory: Tracklet Cleaving and Re-connection by Deep Siamese Bi-GRU for Multiple Object Tracking | 2018 |
| 8 | FWT | 47.8 | Fusion of Head and Full-Body Detectors for Multi-Object Tracking | 2017 |
| 9 | MOTDT | 47.6 | Real-time Multiple People Tracking with Deeply Learned Candidate Selection and Person Re-Identification | 2018 |
| 10 | NOMT | 46.4 | Near-Online Multi-target Tracking with Aggregated Local Flow Descriptor | 2015 |
| 11 | DMMOT | 46.1 | Online Multi-Object Tracking with Dual Matching Attention Networks | 2019 |
| Rank | Model | MOTA | Paper | Year |
|---|---|---|---|---|
| 1 | FairMOT | 67.5 | A Simple Baseline for Multi-Object Tracking | 2020 |
| 2 | Lif_T | 60.5 | Lifted Disjoint Paths with Application in Multiple Object Tracking | 2020 |
| 3 | MPNTrack | 58.8 | Learning a Neural Solver for Multiple Object Tracking | 2020 |
| 4 | DeepMOT | 53.7 | How To Train Your Deep Multi-Object Tracker | 2019 |
| 5 | JBNOT | 52.6 | Multiple People Tracking using Body and Joint Detections | 2019 |
| 6 | TNT | 51.9 | Exploit the Connectivity: Multi-Object Tracking with TrackletNet | 2018 |
| 7 | FWT | 51.3 | Fusion of Head and Full-Body Detectors for Multi-Object Tracking | 2017 |
| 8 | MOTDT17 | 50.9 | Real-time Multiple People Tracking with Deeply Learned Candidate Selection and Person Re-Identification | 2018 |
| Rank | Model | MOTA | Paper | Year |
|---|---|---|---|---|
| 1 | FairMOT | 61.8 | A Simple Baseline for Multi-Object Tracking | 2020 |
| 2 | UnsupTrack | 53.6 | Simple Unsupervised Multi-Object Tracking | 2020 |
LibMOT: a simple mot toolbox for mot research
link is a good course about multiple object tracking. The course is offered as a Massive Open Online Course (MOOC) on edX.